According to Gero AI, sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and stress resilience. The ‘longevity’ startup — which condenses its mission to the pithy goal of “complex hacking diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity. The contention is that a simple measure of ‘steps’ isn’t nuanced enough to predict individual health. Gero’s AI has been trained on large amounts of biological data to spot patterns that can be linked to morbidity risk. It also measures how quickly a person recovers from bodily stress — another biomarker linked to lifespan, i.e., the faster the body recovers from stress, the better the individual’s overall health prognosis.
A research paper Gero published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and demonstrated that its biological age acceleration model was comparable to models based on blood test results. Another paper, due to be published in the journal Nature Communications later this month, will detail its device-derived measurement of biological resilience. The Singapore-based startup, which has research roots in Russia — founded in 2015 by a Russian scientist with an atheoretical physics background—has raised $5 million in seed funding to date (in two tranches).
Backers come from the biotech and AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early-stage fun, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to the Russian drug development firm Valenta. (The pharma company itself is not an investor). Fenichel is a theoretical physicist by training who, after his Ph.D. and some ten years in academia, moved into biotech to work on molecular modeling and machine learning for drug discovery — where he got interested in the problem of aging and decided to start the company.
As well as conducting its biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to humans’ stress — via sensor data captured by mobile devices. “Health, of course, is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you condense human health to one number, then, for many people, the logical age is the best number. It tells you — essentially — how toxic your lifestyle ise… The more biological age you have relative to your chronological age years — biological acceleration — the more are your chances to get a chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”
Gero has recently launched a (paid, for now) AP called GeroSense, which is aimed at health and fitness apps so they can tap up its AI modeling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline). Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero tto feed its AI’s predictive capabilities further and support the more comprehensive research mission — where it hopes to progress anti-aging drug discovery, working in partnerships with pharmaceutical companies.
The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement tool to track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using. “Every health and wellness provider — maybe even a gym — can put into their app,p for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.
“We developed these capabilities because we need to understand how Aging works in humans, not mice. Once we developed it, we used it in our sophisticated genetic research to find genes — we are testing them in the laboratory — but this technology, the measurement of aging from continuous signals like wearable devices, is a good trick. So that’s why we announced this GeroSense project,” he goes on.
“Ageing is this gradual decline of your functional abilities, which is terrible, but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. This means that when you’re [biologically] stressed, you cannot return to the norm as quickly as possible. S,o we report this resilience. So when people start losing this resilience, it means they’re not robust anymore, and the same stress levels as in their 20s would get them [knocked off] the rails.
“We believe this loss of resilience is one of the key aging phenotypes because it tells you that you’re vulnerable to future diseases even before those diseases set in.” “In-house, everything is aging. We are committed to aging: Measurement and intervention,” adds Fedichev. “We want to build something like an operating system for longevity and wellness.” Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with PepsiCo.
He sketches a link between how it hopes to work with insurance companies in the area of health outcomes and how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensors data in the driving seat if you’ll pardon the pun. (“Essentially, we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.) But the nearer-term plan is to raise more funding — and potentially switch to offering the API for free to scale up the data capture potential.
Zooming out for a bit of context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then, a small but growing field of ‘longevity’ startups has sprung up, researching and extending (in the first instance). (Ending death is, clearly, the moonshot atop the moonshot.) Death is still with us, of course. Still, identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.
The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking fitness, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery. Longevity has also seen a bit of an upsurge in interest recently as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically. Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and drug discovery.
Plenty is also — like Gero — trying to use AI and extensive data analysis to understand better and counteract biological Aging, bringing together experts in physics, maths, and natural science to hunt for biomarkers to research further to combat age-related disease deterioration. Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI’ longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).
Gero AI is taking a different tack toward the same overarching goal. By honing in on data generated by activity sensors embedded into everyday mobile devices, people carry (or wear) as a proxy signal for studying their biology. The advantage is that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their health. Instead, our device can passively generate proxy signals for biological study — at a vast scale and at a low cost. So, the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.
And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to stay one step ahead of death, such high-end services won’t scale to the rest of us. Suppose its digital biomarkers live up to Gero’s claims. In that case, its approach could, at the least, help steer millions towards healthier lifestyles while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).
The insurance industry is naturally interested — with the potential for such tools to nudge individuals towards healthier lifestyles and thereby reduce payout costs. For individuals motivated to improve their health, Fedichev says the issue now is it’s tough for people to know precisely which lifestyle changes or interventions best suit their particular biology. For example, fasting has been shown in some studies to help combat biological Aging. However, he notes that the approach may not be practical for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise ea, eating, or avoiding certain foods).
Again, those rules of thumb may have a lot of nuances, depending on an individual’s particular biology. Scientific research is inevitably limited by access to funding. (Research can thus focus on specific groups to exclude others — e.g., men rather than women or the young rather than middle-aged.) This is why Fedichev believes there’s a lot of value in creating a measure that can address health-related knowledge gaps at essentially no individual cost. Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s biological age and resilience measurements. But of course, it hopes to go further — as it ingests more data.
“Technically, it’s not properly different from what we are doing — it just happens that we can do it now because there are such efforts like the UK biobank. Government money and some industries sponsor money; maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, and wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.
“Imagine that for every diet, for every training routine, meditation… to make sure that we can optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.” “When we will have 1M tracks [half a year’s worth of data on 1M individuals], we will combine that with genetics and solve aging,” he adds, with an entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”
Fitness and health apps are a prominent target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science. “We expect that these [apps] will get lots of people, and we will be able to analyze those people for them as a fun feature first, for their users. But in the background,d we will build the best model of human Aging,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)
“We are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have many signals — including from available devices that produce a few years’ long windows on human aging. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us. “We cannot own the treatments because we cannot patent them, but maybe we can own the personalization — the AI that personalized those treatments for you.” From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.
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